"""simple docstring"""



import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
   from PIL import Image
else:






   class  snake_case__      :


    @staticmethod
    def 						a__   ( *lowerCamelCase    ,		**lowerCamelCase						):
           pass



def 	_lowerCamelCase(	a				):
       __a       =    hashlib.mda(image.tobytes()				)
       return m.hexdigest()[:1_0]



def 	_lowerCamelCase(	a				):
       __a       =    np.array(a				)
       __a       =    npimg.shape
       return {"hash": hashimage(a				), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class  snake_case__      (       unittest.TestCase       ):
 _snake_case       :					str							  =						dict(
     (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()       ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])       )
 _snake_case       :					Optional[Any]							  =						dict(
     (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()       ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])       )


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    MaskGenerationPipeline(model=lowerCamelCase    ,		image_processor=lowerCamelCase						)
        return image_segmenter, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        pass


 @require_tf
 @unittest.skip("Image segmentation not implemented in TF"						)
 def 						a__   ( self						):
        pass


 @slow
 @require_torch
 def 						a__   ( self						):
        __a       =    pipeline("mask-generation"    ,		model="facebook/sam-vit-huge"						)

        __a       =    image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"    ,		points_per_batch=256						)

        # Shortening by hashing
        __a       =    []
        for i, o in enumerate(outputs["masks"]						):
               new_outupt += [{"mask": mask_to_test_readable(lowerCamelCase						), "scores": outputs["scores"][i]}]

        # fmt: off
        self.assertEqual(
            nested_simplify(lowerCamelCase    ,		decimals=4						)    ,		[
                {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0444},
                {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.021},
                {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0167},
                {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0132},
                {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0053},
                {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.9967},
                {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.993},
                {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.9909},
                {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.9879},
                {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.9834},
                {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.9716},
                {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.9612},
                {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.9599},
                {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.9552},
                {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.9532},
                {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.9516},
                {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.9499},
                {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.9483},
                {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.9464},
                {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.943},
                {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.943},
                {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.9408},
                {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.9335},
                {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.9326},
                {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.9262},
                {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.8999},
                {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.8986},
                {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.8984},
                {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.8873},
                {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.8871}
            ]    ,		)
        # fmt: on






 @require_torch
 @slow
 def 						a__   ( self						):
        __a       =    "facebook/sam-vit-huge"
        __a       =    pipeline("mask-generation"    ,		model=lowerCamelCase						)

        __a       =    image_segmenter(
            "http://images.cocodataset.org/val2017/000000039769.jpg"    ,		pred_iou_thresh=1    ,		points_per_batch=256						)

        # Shortening by hashing
        __a       =    []
        for i, o in enumerate(outputs["masks"]						):
               new_outupt += [{"mask": mask_to_test_readable(lowerCamelCase						), "scores": outputs["scores"][i]}]

        self.assertEqual(
            nested_simplify(lowerCamelCase    ,		decimals=4						)    ,		[
                {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0444},
                {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0210},
                {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0167},
                {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0132},
                {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0053},
            ]    ,		)

"""simple docstring"""



import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


SCREAMING_SNAKE_CASE__:List[str]									=			logging.getLogger(__name__)



def 	_lowerCamelCase(	):
       __a       =    argparse.ArgumentParser(
           description="Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."				)
       parser.add_argument(
           "--dataset_name"   ,    type=a   ,    default="wikitext"   ,    help="Name of the training. Explore datasets at: hf.co/datasets."   ,    )
       parser.add_argument(
           "--dataset_config"   ,    type=a   ,    default="wikitext-103-raw-v1"   ,    help="Configuration name of the dataset."				)
       parser.add_argument(
           "--tokenizer_name_or_path"   ,    type=a   ,    default="sayakpaul/unigram-tokenizer-wikitext"   ,    help="Tokenizer identifier. Can be a local filepath or a Hub identifier."   ,    )
       parser.add_argument(
           "--shard_size"   ,    type=a   ,    default=1_0_0_0   ,    help="Number of entries to go in a single shard."   ,    )
       parser.add_argument("--split"   ,    type=a   ,    default="train"   ,    choices=["train", "test", "validation"]				)
       parser.add_argument(
           "--limit"   ,    default=a   ,    type=a   ,    help="Limit the number of shards (used for debugging)."   ,    )
       parser.add_argument(
           "--max_length"   ,    type=a   ,    default=5_1_2   ,    help="Maximum sequence length. For training on TPUs, it helps to have a maximum"
           " sequence length that is a multiple of 8."   ,    )
       parser.add_argument(
           "--output_dir"   ,    default="tf-tpu"   ,    type=a   ,    help="Output directory where the TFRecord shards will be saved. If the"
           " path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"
           " shards will be directly saved to a Google Cloud Storage bucket."   ,    )

       __a       =    parser.parse_args()
       return args



def 	_lowerCamelCase(	a				):
       def fn(a				):
              return tokenizer(examples["text"]				)

       return fn



def 	_lowerCamelCase(	a				):
       __a       =    []
       for i in range(len(tokenized_data["input_ids"]				)				):
              __a       =    {
                  "input_ids": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["input_ids"][i]				)				),
                  "attention_mask": tf.train.Feature(
                      intaa_list=tf.train.IntaaList(value=tokenized_data["attention_mask"][i]				)				),
              }
              __a       =    tf.train.Features(feature=a				)
              __a       =    tf.train.Example(features=a				)
              __a       =    example.SerializeToString()
              records.append(a				)
       return records



def 	_lowerCamelCase(	a				):
       __a       =    datasets.load_dataset(args.dataset_name   ,    args.dataset_config   ,    split=args.split				)

       if args.limit is not None:
              __a       =    min(len(a				)   ,    args.limit				)
              __a       =    dataset.select(range(a				)				)
              print(F"Limiting the dataset to {args.limit} entries."				)

       __a       =    AutoTokenizer.from_pretrained(args.tokenizer_name_or_path				)

       # Handle output directory creation.
       # For serializing into a Google Cloud Storage Bucket, one needs to first
       # create a bucket.
       if "gs" not in args.output_dir:
              if not os.path.exists(args.output_dir				):
                     os.makedirs(args.output_dir				)
              __a       =    os.path.join(args.output_dir   ,    args.split				)
              if not os.path.exists(a				):
                     os.makedirs(a				)
       else:
              __a       =    os.path.join(args.output_dir   ,    args.split				)

       # Tokenize the whole dataset at once.
       __a       =    tokenize_function(a				)
       __a       =    dataset.map(a   ,    batched=a   ,    num_proc=4   ,    remove_columns=["text"]				)

       # We need to concatenate all our texts together, and then split the result
       # into chunks of a fixed size, which we will call block_size. To do this, we
       # will use the map method again, with the option batched=True. When we use batched=True,
       # the function we pass to map() will be passed multiple inputs at once, allowing us
       # to group them into more or fewer examples than we had in the input.
       # This allows us to create our new fixed-length samples. The advantage of this
       # method is that we don't lose a whole lot of content from the dataset compared to the
       # case where we simply tokenize with a pre-defined max_length.

       def group_texts(a				):
              # Concatenate all texts.
              __a       =    {k: sum(examples[k]   ,    []				) for k in examples.keys()}
              __a       =    len(concatenated_examples[list(examples.keys()				)[0]]				)
              # We drop the small remainder, though you could add padding instead if the model supports it
              # In this, as in all things, we advise you to follow your heart 🫀
              __a       =    (total_length // args.max_length) * args.max_length
              # Split by chunks of max_len.
              __a       =    {
                  k: [t[i : i + args.max_length] for i in range(0   ,    a   ,    args.max_length				)]
                  for k, t in concatenated_examples.items()
              }
              return result

       __a       =    dataset_tokenized.map(a   ,    batched=a   ,    batch_size=1_0_0_0   ,    num_proc=4				)

       __a       =    0
       __a       =    0
       for shard in range(0   ,    len(a				)   ,    args.shard_size				):
              __a       =    grouped_dataset[shard : shard + args.shard_size]
              __a       =    len(dataset_snapshot["input_ids"]				)
              __a       =    os.path.join(a   ,    F"dataset-{shard_count}-{records_containing}.tfrecord"				)
              __a       =    get_serialized_examples(a				)

              with tf.io.TFRecordWriter(a				) as out_file:
                     for i in range(len(a				)				):
                            __a       =    serialized_examples[i]
                            out_file.write(a				)
                     print("Wrote file {} containing {} records".format(a   ,    a				)				)

              shard_count += 1
              total_records += records_containing

       with open(F"split-{args.split}-records-count.txt"   ,    "w"				) as f:
              print(F"Total {args.split} records: {total_records}"   ,    file=a				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:List[Any]									=			parse_args()
   main(args)
